Our BCNet consists of two GCN layers with a cascaded structure, each respectively regresses the mask and boundaries of the occluding and partially occluded objects. We utilize GCN in our implementation because GCN can consider the non-local relationship between pixels, allowing for propagating information across pixels despite the presence of occluding regions.
The explicit bilayer occluder-occludee relational modeling within the same ROI also makes our final segmentation results more explainable than previous methods. For object detector, we use the FCOS [1]} owing to its efficient memory and running time, while noting that other state-of-the-art object detectors can also be used as demonstrated in our experiments.
